{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 交易系统(Aberration)——趋势跟踪系统股票修改版\n",
    "\n",
    "---\n",
    "\n",
    "### 1.基本原理\n",
    "\n",
    "    股票价格多围绕某一价格上下波动。当股价远离基准价格超过一定幅度则可能形成长期趋势，\n",
    "    而短期股价偏离基准价格过多则可能因过度偏离均值而形成大幅回撤甚至趋势终止。\n",
    "    \n",
    "    由此以观察期内均线作为基准价格，以观察期内标准差的一定倍数作为开仓价或止盈价，采用移动止损方式进行止损构建此策略。\n",
    "\n",
    "\n",
    "- 开仓条件    \n",
    "  \n",
    "  当日最高价 > 均价 +　开仓触发倍数 × 观察期内标准差最大值\n",
    "\n",
    "\n",
    "- 止盈条件  \n",
    "  \n",
    "  当天最高价 > 均价 + 止盈触发倍数 × 观察期内标准差最大值\n",
    "    \n",
    "\n",
    "- 止损条件  \n",
    "  \n",
    "  同样结合了移动止损和固定止损两种止损模式；\n",
    "  \n",
    "  当天最低价 < max(均价， 开仓价 - 止损触发倍数 × 开仓时观察期内标准差最大值) \n",
    "  \n",
    "  均价：移动止损；开仓价 - 止损触发倍数 × 开仓时观察期内标准差最大值：固定止损\n",
    "  \n",
    "  注意：\n",
    "  \n",
    "  \n",
    "- 考虑了开仓当天也触发了平仓信号的近似处理；  \n",
    "\n",
    "\n",
    "- 用观察期内标准差的最大值开仓的原因是：在震荡行情的时候，避免频繁开仓；更加稳定\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### 2.策略实现"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 数据获取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tushare as ts\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [],
   "source": [
    "code = '601688'\n",
    "length = 10             #  参考周期长度，用于确定计算标准差及移动平均的周期\n",
    "open_trigger = 0.25      #  价格向上偏离均线0.5倍观察期内标准差的最大值开仓；\n",
    "stopwin_trigger = 4     #  价格向上偏离均线3倍观察期内标准差的最大值止盈；\n",
    "stoplose_trigger = 0.25    #  移动止损；跌破均值移动止损；固定止损：开仓价向下偏离观察期内标准差的最大值；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "本接口即将停止更新，请尽快使用Pro版接口：https://tushare.pro/document/2\n"
     ]
    }
   ],
   "source": [
    "data = ts.get_k_data(code, '2016-08-19', '2020-08-19')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2016-08-19</td>\n",
       "      <td>19.644</td>\n",
       "      <td>19.855</td>\n",
       "      <td>19.970</td>\n",
       "      <td>19.500</td>\n",
       "      <td>679230.0</td>\n",
       "      <td>601688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-22</td>\n",
       "      <td>19.663</td>\n",
       "      <td>19.567</td>\n",
       "      <td>19.816</td>\n",
       "      <td>19.443</td>\n",
       "      <td>468617.0</td>\n",
       "      <td>601688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>19.567</td>\n",
       "      <td>19.730</td>\n",
       "      <td>19.941</td>\n",
       "      <td>19.356</td>\n",
       "      <td>471206.0</td>\n",
       "      <td>601688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>19.740</td>\n",
       "      <td>19.280</td>\n",
       "      <td>19.807</td>\n",
       "      <td>19.117</td>\n",
       "      <td>568465.0</td>\n",
       "      <td>601688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>19.184</td>\n",
       "      <td>19.337</td>\n",
       "      <td>19.481</td>\n",
       "      <td>19.021</td>\n",
       "      <td>481234.0</td>\n",
       "      <td>601688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    open   close    high     low    volume    code\n",
       "0  2016-08-19  19.644  19.855  19.970  19.500  679230.0  601688\n",
       "1  2016-08-22  19.663  19.567  19.816  19.443  468617.0  601688\n",
       "2  2016-08-23  19.567  19.730  19.941  19.356  471206.0  601688\n",
       "3  2016-08-24  19.740  19.280  19.807  19.117  568465.0  601688\n",
       "4  2016-08-25  19.184  19.337  19.481  19.021  481234.0  601688"
      ]
     },
     "execution_count": 480,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del data['index']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 策略数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['pct_change'] = data['close'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['ma'] = data['close'].rolling(window=length, min_periods=3).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['std'] = data['close'].rolling(window=length, min_periods=3).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>code</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>ma</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2016-08-19</td>\n",
       "      <td>19.644</td>\n",
       "      <td>19.855</td>\n",
       "      <td>19.970</td>\n",
       "      <td>19.500</td>\n",
       "      <td>679230.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-22</td>\n",
       "      <td>19.663</td>\n",
       "      <td>19.567</td>\n",
       "      <td>19.816</td>\n",
       "      <td>19.443</td>\n",
       "      <td>468617.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>-0.014505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>19.567</td>\n",
       "      <td>19.730</td>\n",
       "      <td>19.941</td>\n",
       "      <td>19.356</td>\n",
       "      <td>471206.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.008330</td>\n",
       "      <td>19.717333</td>\n",
       "      <td>0.144417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>19.740</td>\n",
       "      <td>19.280</td>\n",
       "      <td>19.807</td>\n",
       "      <td>19.117</td>\n",
       "      <td>568465.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>-0.022808</td>\n",
       "      <td>19.608000</td>\n",
       "      <td>0.248434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>19.184</td>\n",
       "      <td>19.337</td>\n",
       "      <td>19.481</td>\n",
       "      <td>19.021</td>\n",
       "      <td>481234.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>19.553800</td>\n",
       "      <td>0.246937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    open   close    high     low    volume    code  pct_change  \\\n",
       "0  2016-08-19  19.644  19.855  19.970  19.500  679230.0  601688         NaN   \n",
       "1  2016-08-22  19.663  19.567  19.816  19.443  468617.0  601688   -0.014505   \n",
       "2  2016-08-23  19.567  19.730  19.941  19.356  471206.0  601688    0.008330   \n",
       "3  2016-08-24  19.740  19.280  19.807  19.117  568465.0  601688   -0.022808   \n",
       "4  2016-08-25  19.184  19.337  19.481  19.021  481234.0  601688    0.002956   \n",
       "\n",
       "          ma       std  \n",
       "0        NaN       NaN  \n",
       "1        NaN       NaN  \n",
       "2  19.717333  0.144417  \n",
       "3  19.608000  0.248434  \n",
       "4  19.553800  0.246937  "
      ]
     },
     "execution_count": 484,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以观察期内标准差最大值作为标准差限制指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['std_limit'] = data['std'].rolling(window=length).max()      #观察期内标准差的最大值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由于实盘中当天的日线级别参考指标未实现，例如当日ma计算时使用当日收盘价，因此应使用昨日参考指标指导当日交易"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['yes_ma'] = data['ma'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['yes_std_limit'] = data['std_limit'].shift(1)            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "计算当日开仓价和止盈价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['long_open_price'] = data['yes_ma'] + data['yes_std_limit']*open_trigger    #计算每一天满足条件的开仓价；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['long_stopwin_price'] = data['yes_ma'] + data['yes_std_limit']*stopwin_trigger   #计算每一天满足条件的止盈价；价格高于3倍观察期内标准差最大值止盈；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>code</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>ma</th>\n",
       "      <th>std</th>\n",
       "      <th>std_limit</th>\n",
       "      <th>yes_ma</th>\n",
       "      <th>yes_std_limit</th>\n",
       "      <th>long_open_price</th>\n",
       "      <th>long_stopwin_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2016-08-19</td>\n",
       "      <td>19.644</td>\n",
       "      <td>19.855</td>\n",
       "      <td>19.970</td>\n",
       "      <td>19.500</td>\n",
       "      <td>679230.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2016-08-22</td>\n",
       "      <td>19.663</td>\n",
       "      <td>19.567</td>\n",
       "      <td>19.816</td>\n",
       "      <td>19.443</td>\n",
       "      <td>468617.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>-0.014505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2016-08-23</td>\n",
       "      <td>19.567</td>\n",
       "      <td>19.730</td>\n",
       "      <td>19.941</td>\n",
       "      <td>19.356</td>\n",
       "      <td>471206.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.008330</td>\n",
       "      <td>19.717333</td>\n",
       "      <td>0.144417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2016-08-24</td>\n",
       "      <td>19.740</td>\n",
       "      <td>19.280</td>\n",
       "      <td>19.807</td>\n",
       "      <td>19.117</td>\n",
       "      <td>568465.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>-0.022808</td>\n",
       "      <td>19.608000</td>\n",
       "      <td>0.248434</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.717333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2016-08-25</td>\n",
       "      <td>19.184</td>\n",
       "      <td>19.337</td>\n",
       "      <td>19.481</td>\n",
       "      <td>19.021</td>\n",
       "      <td>481234.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.002956</td>\n",
       "      <td>19.553800</td>\n",
       "      <td>0.246937</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.608000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>2016-08-26</td>\n",
       "      <td>19.414</td>\n",
       "      <td>19.088</td>\n",
       "      <td>19.452</td>\n",
       "      <td>19.021</td>\n",
       "      <td>367654.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>-0.012877</td>\n",
       "      <td>19.476167</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.553800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2016-08-29</td>\n",
       "      <td>18.973</td>\n",
       "      <td>19.184</td>\n",
       "      <td>19.280</td>\n",
       "      <td>18.973</td>\n",
       "      <td>239029.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.005029</td>\n",
       "      <td>19.434429</td>\n",
       "      <td>0.288064</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.476167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>2016-08-30</td>\n",
       "      <td>19.222</td>\n",
       "      <td>19.356</td>\n",
       "      <td>19.500</td>\n",
       "      <td>19.078</td>\n",
       "      <td>402543.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.008966</td>\n",
       "      <td>19.424625</td>\n",
       "      <td>0.268133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.434429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>19.337</td>\n",
       "      <td>19.567</td>\n",
       "      <td>19.653</td>\n",
       "      <td>19.193</td>\n",
       "      <td>441436.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.010901</td>\n",
       "      <td>19.440444</td>\n",
       "      <td>0.255266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.424625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>19.529</td>\n",
       "      <td>19.088</td>\n",
       "      <td>19.615</td>\n",
       "      <td>19.021</td>\n",
       "      <td>396409.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>-0.024480</td>\n",
       "      <td>19.405200</td>\n",
       "      <td>0.265221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.440444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2016-09-02</td>\n",
       "      <td>18.983</td>\n",
       "      <td>19.184</td>\n",
       "      <td>19.347</td>\n",
       "      <td>18.935</td>\n",
       "      <td>331664.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.005029</td>\n",
       "      <td>19.338100</td>\n",
       "      <td>0.219764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.405200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2016-09-05</td>\n",
       "      <td>19.222</td>\n",
       "      <td>19.165</td>\n",
       "      <td>19.366</td>\n",
       "      <td>19.040</td>\n",
       "      <td>301508.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>-0.000990</td>\n",
       "      <td>19.297900</td>\n",
       "      <td>0.209782</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.338100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2016-09-06</td>\n",
       "      <td>19.088</td>\n",
       "      <td>19.174</td>\n",
       "      <td>19.261</td>\n",
       "      <td>18.810</td>\n",
       "      <td>377868.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.000470</td>\n",
       "      <td>19.242300</td>\n",
       "      <td>0.146743</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.297900</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.370763</td>\n",
       "      <td>20.463705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>19.174</td>\n",
       "      <td>19.270</td>\n",
       "      <td>19.548</td>\n",
       "      <td>19.126</td>\n",
       "      <td>403460.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.005007</td>\n",
       "      <td>19.241300</td>\n",
       "      <td>0.146492</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.242300</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.315163</td>\n",
       "      <td>20.408105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>19.232</td>\n",
       "      <td>19.251</td>\n",
       "      <td>19.328</td>\n",
       "      <td>19.088</td>\n",
       "      <td>199009.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>-0.000986</td>\n",
       "      <td>19.232700</td>\n",
       "      <td>0.142725</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.241300</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.314163</td>\n",
       "      <td>20.407105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date    open   close    high     low    volume    code  pct_change  \\\n",
       "0   2016-08-19  19.644  19.855  19.970  19.500  679230.0  601688         NaN   \n",
       "1   2016-08-22  19.663  19.567  19.816  19.443  468617.0  601688   -0.014505   \n",
       "2   2016-08-23  19.567  19.730  19.941  19.356  471206.0  601688    0.008330   \n",
       "3   2016-08-24  19.740  19.280  19.807  19.117  568465.0  601688   -0.022808   \n",
       "4   2016-08-25  19.184  19.337  19.481  19.021  481234.0  601688    0.002956   \n",
       "5   2016-08-26  19.414  19.088  19.452  19.021  367654.0  601688   -0.012877   \n",
       "6   2016-08-29  18.973  19.184  19.280  18.973  239029.0  601688    0.005029   \n",
       "7   2016-08-30  19.222  19.356  19.500  19.078  402543.0  601688    0.008966   \n",
       "8   2016-08-31  19.337  19.567  19.653  19.193  441436.0  601688    0.010901   \n",
       "9   2016-09-01  19.529  19.088  19.615  19.021  396409.0  601688   -0.024480   \n",
       "10  2016-09-02  18.983  19.184  19.347  18.935  331664.0  601688    0.005029   \n",
       "11  2016-09-05  19.222  19.165  19.366  19.040  301508.0  601688   -0.000990   \n",
       "12  2016-09-06  19.088  19.174  19.261  18.810  377868.0  601688    0.000470   \n",
       "13  2016-09-07  19.174  19.270  19.548  19.126  403460.0  601688    0.005007   \n",
       "14  2016-09-08  19.232  19.251  19.328  19.088  199009.0  601688   -0.000986   \n",
       "\n",
       "           ma       std  std_limit     yes_ma  yes_std_limit  long_open_price  \\\n",
       "0         NaN       NaN        NaN        NaN            NaN              NaN   \n",
       "1         NaN       NaN        NaN        NaN            NaN              NaN   \n",
       "2   19.717333  0.144417        NaN        NaN            NaN              NaN   \n",
       "3   19.608000  0.248434        NaN  19.717333            NaN              NaN   \n",
       "4   19.553800  0.246937        NaN  19.608000            NaN              NaN   \n",
       "5   19.476167  0.291451        NaN  19.553800            NaN              NaN   \n",
       "6   19.434429  0.288064        NaN  19.476167            NaN              NaN   \n",
       "7   19.424625  0.268133        NaN  19.434429            NaN              NaN   \n",
       "8   19.440444  0.255266        NaN  19.424625            NaN              NaN   \n",
       "9   19.405200  0.265221        NaN  19.440444            NaN              NaN   \n",
       "10  19.338100  0.219764        NaN  19.405200            NaN              NaN   \n",
       "11  19.297900  0.209782   0.291451  19.338100            NaN              NaN   \n",
       "12  19.242300  0.146743   0.291451  19.297900       0.291451        19.370763   \n",
       "13  19.241300  0.146492   0.291451  19.242300       0.291451        19.315163   \n",
       "14  19.232700  0.142725   0.291451  19.241300       0.291451        19.314163   \n",
       "\n",
       "    long_stopwin_price  \n",
       "0                  NaN  \n",
       "1                  NaN  \n",
       "2                  NaN  \n",
       "3                  NaN  \n",
       "4                  NaN  \n",
       "5                  NaN  \n",
       "6                  NaN  \n",
       "7                  NaN  \n",
       "8                  NaN  \n",
       "9                  NaN  \n",
       "10                 NaN  \n",
       "11                 NaN  \n",
       "12           20.463705  \n",
       "13           20.408105  \n",
       "14           20.407105  "
      ]
     },
     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>ma</th>\n",
       "      <th>std</th>\n",
       "      <th>yes_std_limit</th>\n",
       "      <th>long_open_price</th>\n",
       "      <th>long_stopwin_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2016-09-02</td>\n",
       "      <td>19.184</td>\n",
       "      <td>19.347</td>\n",
       "      <td>0.005029</td>\n",
       "      <td>19.3381</td>\n",
       "      <td>0.219764</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2016-09-05</td>\n",
       "      <td>19.165</td>\n",
       "      <td>19.366</td>\n",
       "      <td>-0.000990</td>\n",
       "      <td>19.2979</td>\n",
       "      <td>0.209782</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2016-09-06</td>\n",
       "      <td>19.174</td>\n",
       "      <td>19.261</td>\n",
       "      <td>0.000470</td>\n",
       "      <td>19.2423</td>\n",
       "      <td>0.146743</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.370763</td>\n",
       "      <td>20.463705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>2016-09-07</td>\n",
       "      <td>19.270</td>\n",
       "      <td>19.548</td>\n",
       "      <td>0.005007</td>\n",
       "      <td>19.2413</td>\n",
       "      <td>0.146492</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.315163</td>\n",
       "      <td>20.408105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>19.251</td>\n",
       "      <td>19.328</td>\n",
       "      <td>-0.000986</td>\n",
       "      <td>19.2327</td>\n",
       "      <td>0.142725</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.314163</td>\n",
       "      <td>20.407105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>19.203</td>\n",
       "      <td>19.538</td>\n",
       "      <td>-0.002493</td>\n",
       "      <td>19.2442</td>\n",
       "      <td>0.134146</td>\n",
       "      <td>0.291451</td>\n",
       "      <td>19.305563</td>\n",
       "      <td>20.398505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date   close    high  pct_change       ma       std  yes_std_limit  \\\n",
       "10  2016-09-02  19.184  19.347    0.005029  19.3381  0.219764            NaN   \n",
       "11  2016-09-05  19.165  19.366   -0.000990  19.2979  0.209782            NaN   \n",
       "12  2016-09-06  19.174  19.261    0.000470  19.2423  0.146743       0.291451   \n",
       "13  2016-09-07  19.270  19.548    0.005007  19.2413  0.146492       0.291451   \n",
       "14  2016-09-08  19.251  19.328   -0.000986  19.2327  0.142725       0.291451   \n",
       "15  2016-09-09  19.203  19.538   -0.002493  19.2442  0.134146       0.291451   \n",
       "\n",
       "    long_open_price  long_stopwin_price  \n",
       "10              NaN                 NaN  \n",
       "11              NaN                 NaN  \n",
       "12        19.370763           20.463705  \n",
       "13        19.315163           20.408105  \n",
       "14        19.314163           20.407105  \n",
       "15        19.305563           20.398505  "
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[10:15, ['date', 'close','high','pct_change', 'ma', 'std', 'yes_std_limit','long_open_price', 'long_stopwin_price']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 计算开仓、止盈信号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['long_open_signal'] = np.where(data['high'] > data['long_open_price'], 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['long_stopwin_signal'] = np.where(data['high'] > data['long_stopwin_price'], 1, 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 策略逻辑\n",
    "\n",
    "#### 策略要点：  \n",
    "    1. 当天有持仓，满足平仓条件进行平仓后，当天不再开仓；\n",
    "    2. 当天无持仓，满足开仓条件则进行开仓。开仓当日如果同时满足平仓条件，以第二日开盘价平仓；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "flag = 0    # 记录持仓情况，0代表空仓，1代表持仓；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 前12个数据因均值计算无效所以不作为待处理数据\n",
    "# 终止数据选择倒数第二个以防止当天止盈情况会以第二天开盘价平仓导致无数据情况发生\n",
    "# 最后一天不再进行操作；可能会面临最后一天开仓之后当天触发平仓，要用下一天开盘价卖出，无法得到； \n",
    "\n",
    "for i in range(12, (len(data)-1)):\n",
    "        # 有持仓进行平仓\n",
    "        if flag == 1:\n",
    "            # 计算止损价格，取均线和开仓价下移一定倍数标准差，两者的最大值作为止损价\n",
    "            stoplose_price = max(data.loc[i,'yes_ma'], long_open_price - long_open_delta * stoplose_trigger)    \n",
    "            # 多头止盈并计算当日收益率\n",
    "            if data.loc[i, 'long_stopwin_signal']: \n",
    "                data.loc[i, 'return'] = data.loc[i, 'long_stopwin_price']/data.loc[i-1, 'close'] - 1\n",
    "                flag = 0\n",
    "                \n",
    "            \n",
    "            # 多头移动止损并计算当日收益率\n",
    "            elif data.loc[i, 'low'] < stoplose_price:    \n",
    "            # 考虑到当天开盘价就低于止损价，无法止损的情况；\n",
    "            # 谨慎起见，在计算收益时取止损价和开盘价的最小值；\n",
    "                data.loc[i, 'return'] = min(data.loc[i, 'open'], stoplose_price)/data.loc[i-1, 'close'] - 1\n",
    "                flag = 0\n",
    "            # 多头持仓并计算收益率\n",
    "            \n",
    "            else: \n",
    "                data.loc[i, 'return'] = data.loc[i, 'close']/data.loc[i-1, 'close'] - 1\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        # 无持仓进行开仓\n",
    "        else:\n",
    "            if data.loc[i, 'long_open_signal']:    \n",
    "                #开仓时标记flag=1\n",
    "                flag = 1\n",
    "                # 需要比较当天的开盘价和开仓价，当开盘价高于开仓价时，只能以开盘价进行开仓，不能用开仓价;\n",
    "                # 否则对导致策略收益高估；\n",
    "                # 记录开仓价\n",
    "                long_open_price = max(data.loc[i, 'open'], data.loc[i, 'long_open_price'])    \n",
    "#                 long_open_price = data.loc[i, 'long_open_price']  #存在问题；\n",
    "                # 记录开仓时的10天内的标准差的最大值；是为了计算固定止损的价格；\n",
    "                long_open_delta = data.loc[i, 'yes_std_limit']\n",
    "                # 记录当天盈利情况\n",
    "                data.loc[i, 'return'] = data.loc[i, 'close']/long_open_price - 1\n",
    "                \n",
    "                                \n",
    "                # 计算止损价：多头移动止损，以均线和开仓价减一定倍数标准差，两者的最大值作为止损点\n",
    "                stoplose_price = max(data.loc[i,'yes_ma'], long_open_price - long_open_delta * stoplose_trigger)\n",
    "                # 如果开仓当天同时满足平仓条件，则以第二天开盘价平仓\n",
    "                # 这里做了一定的近似处理；\n",
    "                if (data.loc[i, 'low'] < stoplose_price     # 满足止损条件\n",
    "                 or data.loc[i, 'long_stopwin_signal']):    # 满足止盈条件\n",
    "                        # 记录此次操作盈利情况并将收益记录在开仓日\n",
    "                        data.loc[i, 'return'] = data.loc[i+1, 'open']/long_open_price - 1    \n",
    "                        flag = 0\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>volume</th>\n",
       "      <th>code</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>ma</th>\n",
       "      <th>std</th>\n",
       "      <th>std_limit</th>\n",
       "      <th>yes_ma</th>\n",
       "      <th>yes_std_limit</th>\n",
       "      <th>long_open_price</th>\n",
       "      <th>long_stopwin_price</th>\n",
       "      <th>long_open_signal</th>\n",
       "      <th>long_stopwin_signal</th>\n",
       "      <th>return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>967</td>\n",
       "      <td>2020-08-12</td>\n",
       "      <td>20.66</td>\n",
       "      <td>20.70</td>\n",
       "      <td>20.81</td>\n",
       "      <td>20.33</td>\n",
       "      <td>740679.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.002421</td>\n",
       "      <td>21.056</td>\n",
       "      <td>0.397554</td>\n",
       "      <td>0.673963</td>\n",
       "      <td>21.086</td>\n",
       "      <td>0.673963</td>\n",
       "      <td>21.254491</td>\n",
       "      <td>23.781854</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>968</td>\n",
       "      <td>2020-08-13</td>\n",
       "      <td>20.71</td>\n",
       "      <td>20.63</td>\n",
       "      <td>20.90</td>\n",
       "      <td>20.51</td>\n",
       "      <td>526587.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>-0.003382</td>\n",
       "      <td>21.056</td>\n",
       "      <td>0.397554</td>\n",
       "      <td>0.673963</td>\n",
       "      <td>21.056</td>\n",
       "      <td>0.673963</td>\n",
       "      <td>21.224491</td>\n",
       "      <td>23.751854</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>969</td>\n",
       "      <td>2020-08-14</td>\n",
       "      <td>20.53</td>\n",
       "      <td>20.92</td>\n",
       "      <td>20.94</td>\n",
       "      <td>20.48</td>\n",
       "      <td>552730.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.014057</td>\n",
       "      <td>21.069</td>\n",
       "      <td>0.389942</td>\n",
       "      <td>0.603457</td>\n",
       "      <td>21.056</td>\n",
       "      <td>0.673963</td>\n",
       "      <td>21.224491</td>\n",
       "      <td>23.751854</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>970</td>\n",
       "      <td>2020-08-17</td>\n",
       "      <td>21.46</td>\n",
       "      <td>21.72</td>\n",
       "      <td>22.26</td>\n",
       "      <td>21.26</td>\n",
       "      <td>1853080.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>0.038241</td>\n",
       "      <td>21.109</td>\n",
       "      <td>0.436309</td>\n",
       "      <td>0.569348</td>\n",
       "      <td>21.069</td>\n",
       "      <td>0.603457</td>\n",
       "      <td>21.219864</td>\n",
       "      <td>23.482827</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.010252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>971</td>\n",
       "      <td>2020-08-18</td>\n",
       "      <td>21.68</td>\n",
       "      <td>21.51</td>\n",
       "      <td>21.77</td>\n",
       "      <td>21.38</td>\n",
       "      <td>800984.0</td>\n",
       "      <td>601688</td>\n",
       "      <td>-0.009669</td>\n",
       "      <td>21.118</td>\n",
       "      <td>0.444292</td>\n",
       "      <td>0.488894</td>\n",
       "      <td>21.109</td>\n",
       "      <td>0.569348</td>\n",
       "      <td>21.251337</td>\n",
       "      <td>23.386390</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date   open  close   high    low     volume    code  pct_change  \\\n",
       "967  2020-08-12  20.66  20.70  20.81  20.33   740679.0  601688    0.002421   \n",
       "968  2020-08-13  20.71  20.63  20.90  20.51   526587.0  601688   -0.003382   \n",
       "969  2020-08-14  20.53  20.92  20.94  20.48   552730.0  601688    0.014057   \n",
       "970  2020-08-17  21.46  21.72  22.26  21.26  1853080.0  601688    0.038241   \n",
       "971  2020-08-18  21.68  21.51  21.77  21.38   800984.0  601688   -0.009669   \n",
       "\n",
       "         ma       std  std_limit  yes_ma  yes_std_limit  long_open_price  \\\n",
       "967  21.056  0.397554   0.673963  21.086       0.673963        21.254491   \n",
       "968  21.056  0.397554   0.673963  21.056       0.673963        21.224491   \n",
       "969  21.069  0.389942   0.603457  21.056       0.673963        21.224491   \n",
       "970  21.109  0.436309   0.569348  21.069       0.603457        21.219864   \n",
       "971  21.118  0.444292   0.488894  21.109       0.569348        21.251337   \n",
       "\n",
       "     long_stopwin_price  long_open_signal  long_stopwin_signal    return  \n",
       "967           23.781854                 0                    0       NaN  \n",
       "968           23.751854                 0                    0       NaN  \n",
       "969           23.751854                 0                    0       NaN  \n",
       "970           23.482827                 1                    0  0.010252  \n",
       "971           23.386390                 1                    0       NaN  "
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 计算策略收益率并可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['return'].fillna(0, inplace=True)\n",
    "data['strategy_return'] = (data['return'] + 1).cumprod()\n",
    "data['stock_return'] = (data['pct_change'] + 1).cumprod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bb76ed0>"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['stock_return','strategy_return']].plot(figsize=(12,8), grid =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bb81d90>"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['close'].plot(figsize=(12,8), grid =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### 5.策略编写与实盘差异\n",
    "\n",
    "#### 5.1 策略账号操作对盘面价格的影响\n",
    "\n",
    "    未考虑交易滑点。实盘交易中，如果满足操作条件时实盘成交量较小，策略账号进行操作可能会对盘面价格产生一定影响，导致当次操作成交均价偏离期望成交价，由此产生滑点（一般对收益产生负影响, 策略回测表现优于实盘表现）。\n",
    "    \n",
    "#### 5.2 交易费用\n",
    "     \n",
    "    未考虑交易手续费。对于交易频率较低的策略，交易手续费对回测结果的影响较小；对于交易频率较高的策略，交易手续费可能决定策略回测结果是否为正。（策略回测表现优于实盘表现）\n",
    "    \n",
    "#### 5.3 策略回测执行价实盘是否执行\n",
    "\n",
    "1.期望开仓价是否执行  \n",
    "\n",
    "    本策略回测中期望开仓价定为均线价格加一定倍数的标准差，实盘中可能面临以下情况：\n",
    " - 开仓执行当日开盘时涨停，实盘中无法成交。\n",
    " - 期望开仓价恰好为涨停价，实盘中可能无法成交。\n",
    " - 期望开仓价距离涨停价较近，实盘中价格上涨突破期望开仓价时因交易不活跃等原因直接跳价到涨停价或因交易系统产生并发送交易信号时间慢于价格快速上涨到涨停的时间，实盘中无法成交。  \n",
    "    \n",
    "    \n",
    "2.期望止损价是否执行  \n",
    "\n",
    "    本策略回测中期望止损价定为均线价格和开仓价格的最大值，实盘中可能面临以下情况：\n",
    " - 止损执行当日开盘价低于期望止损价，满足止损条件，应以开盘价执行。（期望止损价更优于开盘价，策略回测表现优于实盘表现）\n",
    " - 止损执行当日开盘时跌停，实盘中无法成交。\n",
    " - 期望止损价恰好为跌停价，实盘中可能无法成交。\n",
    " - 期望止损价距离跌停板较近，实盘中价格下跌突破期望止损价时因交易不活跃等原因直接跳价到跌停价或因交易系统产生并发送交易信号时间慢于价格快速下跌到跌停的时间，实盘中无法成交。  \n",
    "    \n",
    "    \n",
    "#### 5.4 策略信号执行优先级\n",
    "\n",
    "    因策略回测使用日线级数据，当日内同时满足多个操作条件，如同时满足开仓信号、止损信号，回测将无法辨别信号出现先后顺序，由此使用信号执行优先级方式近似计算策略收益情况。该种方式将产生一定偏差。\n",
    "    \n",
    "1.开仓当日同时满足开仓、止损条件\n",
    "\n",
    "    策略回测中，如开仓当日同时满足开仓、止损条件，则假设进行一次完整交易，先开仓，后以第二日开盘价平仓。\n",
    "    实盘中当日如果先满足止损条件（此时未持仓），后满足开仓条件，则第二日不进行平仓操作。（因策略收益特点类似看涨期权，因此实盘表现可能优于策略回测表现）\n",
    "    \n",
    "2.平仓当日同时满足止盈、止损条件\n",
    "\n",
    "    策略回测中，如平仓当日同时满足止盈、止损条件，则以止盈价进行平仓。\n",
    "    实盘中当日如果先满足止损条件（此时有持仓），后满足止盈条件，则以止损价进行平仓操作。（策略回测表现优于实盘表现）"
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